class_docstr: Dataset wrapping tensors.
  
  Each sample will be retrieved by indexing tensors along the first dimension.
  
  Arguments:
      *tensors (Tensor): tensors that have the same size of the first dimension.
code: def __len__(self):
      return self._len
  
imports: import contextlib
  import numbers
  import os
  import random
  import warnings
  from typing import IO, Any, BinaryIO, Optional, Union
  import numpy
  import torch
  from torch.nn.utils.rnn import PackedSequence
  from torch.utils.data import Dataset
  from poutyne.warning_manager import warning_settings
label_desc: Use image node_id 3 for calling the TensorDataset obj's underlying member method code with example usage: obj.__len__() and returns: self